Cross-streamer wavefield interpolation using deep convolutional networks
Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these setti...
| Published in: | SEG Technical Program Expanded Abstracts 2019 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Book Part |
| Language: | English |
| Published: |
SEG
2019
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10852/77027 http://urn.nb.no/URN:NBN:no-80150 https://doi.org/10.1190/segam2019-3214009.1 |
| Summary: | Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these settings. In addition, large streamer separation introduce aliasing of spatial frequencies across the streamers. A new marine survey configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem. However, introduction of near offset data has shown to be challenging for interpolation and regularization, using conventional methods. In this paper, we investigate deep learning as a tool for interpolation beyond spatial aliasing across the streamers, in the shot domain. The proposed method is based on imaging techniques from single-image super resolution (SISR). The model architecture consist of a deep convolutional neural network (CNN) and a periodic resampling layer for upscaling to the non-aliased wavefield. We demonstrate the performance of proposed method on representative broad-band synthetic data and TopSeis field data from the Barents Sea. |
|---|